Temperature-resilient solid-state organic artificial synapses for neuromorphic computing
| Autor: | Hye Ryoung Lee, Garrett LeCroy, H. v. Loo, Iuliana P. Maria, Tyler J. Quill, Armantas Melianas, Yaakov Tuchman, Iain McCulloch, Scott T. Keene, Alberto Salleo, Alexander Giovannitti |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Computer Science::Neural and Evolutionary Computation Materials Science 02 engineering and technology Hardware_PERFORMANCEANDRELIABILITY 010402 general chemistry 01 natural sciences Noise (electronics) Hardware_INTEGRATEDCIRCUITS Electronics Research Articles Applied Physics Resistive touchscreen Multidisciplinary Artificial neural network business.industry Dynamic range Electrical engineering Linearity SciAdv r-articles 021001 nanoscience & nanotechnology 0104 chemical sciences Nonlinear system Neuromorphic engineering 0210 nano-technology business Research Article |
| Zdroj: | Science Advances Science Advances, 6(27):eabb2958. AMER ASSOC ADVANCEMENT SCIENCE |
| ISSN: | 2375-2548 |
| Popis: | Ion gels enable temperature-stable and high-performance organic memories for integration into hardware artificial neural networks. Devices with tunable resistance are highly sought after for neuromorphic computing. Conventional resistive memories, however, suffer from nonlinear and asymmetric resistance tuning and excessive write noise, degrading artificial neural network (ANN) accelerator performance. Emerging electrochemical random-access memories (ECRAMs) display write linearity, which enables substantially faster ANN training by array programing in parallel. However, state-of-the-art ECRAMs have not yet demonstrated stable and efficient operation at temperatures required for packaged electronic devices (~90°C). Here, we show that (semi)conducting polymers combined with ion gel electrolyte films enable solid-state ECRAMs with stable and nearly temperature-independent operation up to 90°C. These ECRAMs show linear resistance tuning over a >2× dynamic range, 20-nanosecond switching, submicrosecond write-read cycling, low noise, and low-voltage (±1 volt) and low-energy (~80 femtojoules per write) operation combined with excellent endurance (>109 write-read operations at 90°C). Demonstration of these high-performance ECRAMs is a fundamental step toward their implementation in hardware ANNs. |
| Databáze: | OpenAIRE |
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